Artificial Intelligence

How new technologies are being developed every year, but organisations are still hungry for insights?

New technologies are being developed every year, but organizations are still hungry for insights on what customers want. Consumers have become the "new king" in the world of business. While industries have changed dramatically over time, it is clear that companies will continue to need information on their customers to be successful. The answer? To study consumers better than ever before!

While there are many different ways to get these insights, organizations can employ emerging technologies to better understand their customers. For example, Artificial Intelligence (AI) gives companies the ability to use big data and transform it into actionable insights.

The Insights Deprived

Organizations are hungry for insights into what customers want, but there is a lot of information available about people in today's world. What's keeping companies from having the correct data? When organizations think about insights, they often focus on customer experience or feedback polls. However, when these sources produce unreliable results, they can close off opportunities to know their customers more nuancedly. Today's modern organizations are insights deprived; they cannot collect data efficiently and utilize it in meaningful ways. Insights are critical for companies who don't necessarily know what the customer wants because companies will not be able to grow into tomorrow without them.

Insights, while seemingly simple to the naked eye, are an extensive topic. Insights about improving products and services can be discovered by just observing consumers or focusing on what they're doing at the moment. Insights aren't just about getting feedback from people but also understanding why customers act the way they do when it comes to what they're interested in or not. Insights also cover a more extensive scope, as significant purchases are made for reasons that go beyond just the product itself. For instance, consumers may want more environmentally friendly appliances because it shows how much energy is being used and therefore benefits society. Insights can be gathered from different sources, but there are some significant roadblocks to getting them. Insights come from a variety of sources, which is part of the problem. Insights often fall short because they're based on what customers say or do at just one moment in time and aren't necessarily indicative of how they will act in the future. In addition, companies often face challenges when it comes to getting more than one perspective. Insights are an essential part of business, but not all sources or types of data collection yield effective results on their own. Insights can be drawn from observations of consumers' products, brand involvement, and even how products are used. Insights also come from market research (surveys or polls) and statistical data analysis to create reports. Insights are not easy to uncover, but they can be obtained through modern technological advancements.

Insights are challenging to gain because organizations aren't always using the right tools to get the results. Insights often rely on consumer feedback, but companies learn biased because customers don't always say what they mean. Insights would be easier to detect if companies paid attention to all the information that technology has available, not just a tiny fraction of it. Insights are crucial in today's global economy because it helps companies know whether their products and services meet consumers' needs. Insights are an essential part of business, but if companies fail to gather them, they cannot grow into tomorrow.

Organizations have a lot of data available about their customers, but this doesn't mean that the information is accurate. It may be possible for organizations to miss out on collecting insights without even knowing! Insights are the key to future growth because they can be garnered through studying consumers better than ever before, not just by relying on consumer feedback. Insights come from different sources, but organizations need to use them correctly not to be misguided. Insights are about understanding why customers act the way they do and what they're interested in, rather than just getting feedback from them. Insights don't necessarily come from what customers say or do at one moment in time, but this is often where companies get their wrong ideas from. Insights are not easy to gain because they often rely on consumer feedback, and organizations aren't always using the right tools to get the results they need. Nevertheless, insights are crucial in today's world because it helps companies know whether their products and services meet consumers' needs.

Artificial Intelligence the way forward

Artificial intelligence (AI) has the potential to provide insights hungry organizations with information on their customers. AI offers advantages in many areas, such as predictive analytics and intelligence. It can be used to identify existing or potential future trends. This can be done using customer data and detecting patterns of behaviors for different groups of people. AI can see consumer behavior and notice which detail is essential, such as preferences related to specific brands, colors, sizes, etc. It also provides the standard features that allow companies to "talk" to their customers through digital channels and increase sales conversion rates.

AI takes existing trends and applies algorithms to find patterns for use in targeted marketing. This can help to optimize an organization's budget because activities are less likely to be wasteful. For example, AI can use customer data to detect when people are most likely to purchase online. Predictive analytics is also beneficial because it provides organizations with the ability to allocate budgets more effort example, this very. For example, this can be used to determine which incentives are likely to influence customers during specific moments in time, such as reminding customers of upcoming birthdays or anniversaries. These are only a few of the benefits that AI offers because it can provide insights hungry organizations with information on their customers.

However, AI is not without flaws. It might be challenging to identify opportunities to scale up when they are poorly executed for unknown reasons. Therefore, it is essential that organizations use data-driven results rather than just basing their decisions on opinions or traditional trends. Organizations need to recognize that customers are individuals with different personal preferences, and it is essential to be mindful of their diversity. In addition, there are limitations regarding how much information companies can gather from customers. For instance, if a consumer does not want to receive personalized advertisements via email or other communication channels, they may opt-out altogether of communication with the company. It is also difficult for organizations to maintain a solid relationship with their customers because brands should be authentic. Consumers can tell when a company is being fake and will eventually lose trust in the brand.

In conclusion, it should be noted that new technologies are constantly being developed to help organizations better understand their customers to market more effectively. However, there are gaps AI has and limitations on how much information can be gathered from customers. Organizations need to make sure they use data-driven results rather than opinions or traditional trends when making decisions. Customers are individuals with different personal preferences, and brands need to be authentic; otherwise, consumers may lose trust in the brand.

The need for Augmented Intelligence in a modern organization

The rise of robots and AI taking over humans is a hot and contested topic in the digital world. However science is still at its infancy stage and it is hard to predict the effect of AI on our day-to-day life. A lot more research needs to be done in this field before we can see Artificial intelligence as a threat or opportunity for humanity. The real benefit AI has been enabling humanity with added capabilities which weren't possible in earlier decades. One of the key areas in AI is the Augmented intelligence that has the potential to change how we live, learn and work. Augmented Intelligence will enhance our day-to-day lives by making us more productive, giving us a competitive edge in life and offering an opportunity for growth of humanity's capabilities.

With research still ongoing about what AI means for humans it is hard to predict where this technology will lead or when we'll start seeing its effects on society as a whole. Augmentation which was not possible previously lets people achieve new goals that were not available before with human abilities alone. Augmenting the minds may be one way forward into future generations if artificial intelligence can offer benefits rather than threats to the humankind then augmentation seems like inevitable step.

Imagine a future where Augmented Intelligence is commonplace. Augmented intelligence will be an integral part of our daily lives, from the moment we wake up to the time that we finally get off work for the day. What does this mean for businesses? It means that they too will need to understand Augmented Intelligence and how it can help them in their own operations. 

Augmented intelligence will give them the ability to make quick decisions as information is rapidly changing and emerging. In addition, augmented Intelligence means businesses can scale their AI efforts, with less work for humans involved in data analysis.

Augmented Intelligence will play an increasingly integral role as we continue our journey into what some are calling The Fourth Industrial Revolution (Industry I through III were revolutions of mechanization and automation). Augmented Intelligence will help companies solve the problem of a shortage of data scientists, as those employees are hard to find with today's rapid progression. 

How Augmented intelligence can help your business: 

- Augmented Intelligence increases efficiency in decision making

- Augmented Intelligence means businesses can use their workforce more efficiently by relying on Augmented Intelligence to handle data analytics

- Augmented intelligence can enable organizations to provide insights in real-time that would otherwise take humans hours or days to find. 

Organizations embarking on Augmented Intelligence journey need to appreciate the three dimensions: Augmented Insight, Augmented Connection and Augmented Imagination.

Augmented connection: Augmented Intelligence can help people connect in new ways and share more complex data to find insights that would otherwise be missed. Augmented Connection means businesses can take advantage of Augmented Intelligence to scale human tasks, freeing up time for more complex ones like strategic thinking and creativity.

Augmented Insight: Augmented intelligence is helping companies to make quick decisions as information changes rapidly during the day without slowing down or being affected by bias. Augmentation insight means Augmented Intelligence is giving businesses the ability to leverage data in a faster, more efficient way. Augmented intelligence allows insights into an organization's processes and how it can better serve customers.

Augmented Imagination: Augmented intelligence will help organizations imagine innovative ideas that they might not otherwise have come up with without Augmenting their imagination. Augmented Imagination means Augmented Intelligence will help organizations to be able to create new products and services that would otherwise not have been possible for humans for hours or days to find. 

The Future of Augmenting Human Understanding: Augmentation in the Workplace 

The reality is that humans will need to augment themselves with AI for the sake of their future. Augmentation in the workplace is a new trend that will change how people work, with Augmented Reality (AR) changing lives for better or worse. Augmented Reality will disrupt how work is done, with Augmenting imagination and creativity that would otherwise not have existed. Augmentation in the workplace means Augmented Intelligence has a place for humans to be able to thrive in an AI-dominated world.

The Augmented Intelligence revolution is already here, and it's only going to get more prevalent as time goes on. Augment your business now, or be left in the dust of those who do!

Takeaway: Augmented Intelligence is the future of data analytics for any organization looking to stay competitive in their industry. Organizations need to understand what it means for them as a business model because they too will Augment with Augmented Intelligence.

The critical task in an AI-led organisation

Artificial intelligence has been used in the last decade in the pursuit of human-like machine learning, but AI is just now seeing a surge as companies worldwide recognize its potential. Artificial Intelligence (AI) provides natural intelligence and smartness to machines that can think, act and decide as humans do; this artificial intelligence is referred to by some businesses as "smart technologies". With successful demonstrations of this technology's capabilities over the years leading up to recent demands from many industries, it seems clear that there will be increased use of what we call “AI-led organizations”.

The perfect harmony

AI is all about data these days. Well, you would be wrong. AI is only as good as the data it uses to make decisions-- and if that data is bad or incomplete, AI can't do its job very well at all. Data management matters for AI just as much as it does for any other business function. That's because there are many facets of data management, from finding the right information to managing that information in a way that improves efficiency and quality through governance practices such as ensuring high-quality metadata (data about your data), designing workflows around best practices. Hence, people know what they're looking at when they review it, making sure there's an audit trail if someone needs to go back and find trails and focus on explainability. AI is a great way to make decisions and take action on data, but AI can't do that without high-quality data management.

Data matters because AI needs it for decision-making. Data also plays an important role in the workflow of AI systems--from finding good information through designing workflows around best practices so people know what they're looking for.

The issue at hand:

AI can't do this without high-quality data. AI needs robust, clean and structured data to make decisions, create new content or products, identify problems in systems and operations before they happen--and take action on that information quickly enough to avoid disruptions.

AI will be able to learn from mistakes when we get better at managing the quality of data. However, data management practices are the backstage work in the context of AI. For example, in a play, front artists drive an audience to cheer and come back for more--and pay for tickets to be entertained by them again. Similarly, AI needs quality data governance to make good decisions and avoid ethics and privacy concerns.

Recommended best practices:

Data governance is fundamental to AI-led organization’s data management and efficiency using the data. Master Data Management (MDM) with a controlled mechanism is essential for efficient data management. It requires technologies, processes, and people to manage and protect the confidential data from, guaranteeing the understandable, complete, secure, correct, discoverable business data. Data governance includes data architecture, data modeling & design, data security, data storage & operations, data integration & interoperability, documents & content, reference & master data, meta-data, data warehousing & business intelligence, and data quality.

The primary goals of data governance are

●     implementing compliance,

●     establishing data usage rules,

●     minimizing risks, reducing costs,

●     improving communication,

●     increasing data value, and

●     risk management and optimization

Some AI-driven organizations have demonstrated that quality data governance practices can lead to AI success. For instance, one of the large technology organizations has shown that it is possible to generate up to $100M in savings from AI adoption alone with the right approach and investment. This includes a focus on adopting AI for predictive maintenance and automation, as well as focusing attention on data governance practices.

Though data governance has advantages, it affects the organization at strategic, operational, and tactical levels. The first dirty and challenging task for AI-led organizations is to convince stakeholders of a data governance budget. An open corporate culture requires changes in the existing organization’s structure that may add to political issues. Lastly, the demands for flexible operations and networks within the organization are necessary for the rapidly changing business requirements that need to implement data governance standards as per the company’s business requirements. 

Artificial intelligence has the potential to make or break a company. AI can enable an organization’s competitive advantage and help it maintain that edge by identifying problems before they happen--but this only works if there are excellent data management practices. Organizations need high-quality data governance practices to evolve and scale the organization and create competitive advantages.

Summary: Managing the dirty task in an AI-led Organisation:

All employees within the organization need to accept and understand the technical and business aspects of data governance and it is essential for AI-led organizations, The dirty or low ROI (in short term) tasks of data management, administration, analysis, and data strategy to ensure improved business opportunities and performance. It also needs an additional workforce with expertise in data governance, such as Master data practitioners, data stewards, governance, ethics, and data quality professionals.

The last call to adopt AI

To use AI tomorrow, you need to embark or extrapolate on your AI journey today—a journey that begins with a fundamental decision to remodel and repurpose your business with AI at the core.
— Anees Merchant

The coming decade will have no patience for businesses with old ways of working. Companies that continue to deliberate on adopting AI technologies will find themselves brutally snared between digital-born companies with inherent AI drivers and early adopters with highly operational AI, steadily losing customers over a widening gap in quality and intuitiveness of offerings. The time for organizations to be on the fence with AI is over.

The Covid pandemic has come when AI technologies have started maturing, with even scaled use-cases in several companies. Many businesses that had barely dabbled with AI as experiments before 2020 are now turning to rapid digital and AI-led transformations to tide over new economic challenges. As these small and local to large and multinational businesses accelerate their digital journeys, their capabilities have started to surpass their peers. Meanwhile, consumers with fast-growing digital savvy are increasingly choosing to interact with companies that provide more value, faster delivery, and a more intuitive purchase journey – all of which are enhanced by AI in a digital world. The AI Divide – that differentiates businesses that use AI intelligently versus those that don’t – is growing wider each day and will increasingly determine business success in every market and industry.

To survive and thrive in the next decade, businesses are going to adopt AI. And to use AI tomorrow, you need to embark or extrapolate on your AI journey today—a journey that begins with a fundamental decision to remodel and repurpose your business with AI at the core.

Let’s take a look at what’s holding many traditional businesses back and how the hurdles can be overcome.

The Building (or Stumbling) Blocks of AI Adoption 

  1. Culture – There was a time when people said, “Culture eats strategy for breakfast, ” which implied that no amount of strategy change would work until you first changed your organization’s culture. But today, if you wait for the culture to change, there may be no breakfast left to eat! People often question their organization’s culture or their executives for failure to change. Remember, if your organization lasted and thrived the pandemic, there must be some goodness in it that has brought you this far. Why not focus on the good and use that as a starting point – How can you use AI to make your organizational strengths even more substantial? This way, you are augmenting what’s already there, and it’s easier because radically changing culture, attitudes, and perceptions is the most challenging thing to do in the world – and you can’t afford to let that process hold you back.

  2. People – The people who enable your AI processes are the fundamental blocks of your AI journey. Currently, there is a talent war growing, and there are severe talent shortages. Companies need to adopt a multi-level strategy to build teams. Organizations need to take a multi-pronged approach, assign a champion to lead the organization’s AI strategy, hire individuals (only if there are no available resources in-house), and infuse the team with business team members, ensuring the culture and actual business alignments. However, it is not always essential or feasible to hire AI specialists for everything. Businesses must realize you cannot do everything in-house, and you should not reinvent the wheel. It would be best to have an ecosystem with partner organizations to support your business’s exact needs and nature. These partner organizations could either be strategic business partners, technology platform providers, academia, or industry cohorts. The important thing is to not allow a shortage of internal resources to become a stumbling block and to move ahead by building your ecosystem.

  3. Technology – As a company, once you have made a fundamental decision to start actively adopting AI in your business process or model, the actual move to AI need not involve a complete one-time overhaul of systems or migration to one large platform that requires massive investment. Companies must take a “Lego approach,” that is, they must build their AI capabilities as a Lego structure within the organization. That way, you can quickly replace the systems or completely change your “Lego” structure pretty promptly and efficiently, and you don’t have to incur massive spending and drive wide-scale adoption all at one go. You could also leverage open source technologies like Python, R, Java, etc., making it easier to experiment and switch – unlike when you invest in a paid technology, you get tied to that company and are limited by what that company or technology can do. The key to technology adoption is to adopt a phased approach to derive the maximum business benefits that current technology can offer while keeping the door open to adopting new and evolving technologies in the future.

    The other important thing is to avoid benchmarking with substantial companies like Google or Amazon because their entire scale and journey are different. You have to identify what’s right for your organization, what AI means for you and keep evolving that statement as your organization matures.

  4. Data – When considering the prospect of using advanced analytics or AI, people often say, “we don’t have the data” or “we have bad data” to begin with. Yet, it’s rare that companies have no usable data. There is always enough data, to begin with. The real problem is when people have not identified the best use cases for AI or the most critical business challenges, to which existing data can then be mapped. It’s always the data inventory and data cataloging exercise that is vital for an organization to push forward. And you can always reach out to external consultants to do that for you.

Setting the Parameters of Success

  1. Accountability – Eventually, all AI strategy starts at the top. And this means that the CEO, CAOs, CDOs, and CIOs must be held accountable for AI investments. As this is your senior leadership, there’s no one else within the organization who can assess the investments’ value and success. Also, a CEO may only think they are answerable to shareholders who may not think long-term, while the AI journey requires a short-, mid-and long-term approach. One solution would be to have an additional board member or an advisor who focuses on this aspect. The other solution would be to have a ‘business performance scorecard’ that is entirely transparent within the organization, so the success parameters are well established and work as a clear guide and yardstick for the company’s AI initiatives. Most importantly, these parameters should provide room for failure because the trial is the only way to find what works best for your organization. And that brings us to ROI.

  2. Return on Investment (ROI) – In my opinion, when you define your company’s budget for AI, at least half the budget should be allocated for ‘learning’ in the first couple of years. So, for instance, if you assign $100 for AI initiatives, you must expect ROI on only $50 and consider the other $50 as a “learning budget” that, in turn, helps you recover your additional $50. That sort of equal focus on immediate returns and continuous learning helps build a genuinely workable, valuable AI journey.  

Conclusion

Today there are many well-recognized, proven use-cases in every part of the organization, from sales and marketing to supply chain, finance, HR, customer service, and others. And these are not small use-cases; many organizations have been able to scale them pretty effectively. All you need to do is begin somewhere.   

2020 has proven that businesses cannot afford to be in the deliberation stage over digital transformation anymore. The time to wait and watch on AI is over. Start or perish…

Digital Transformation: Not about Technology

The last decade gave birth to technologies, sciences, and business models which have brought in 

  • Companies which are challenging the norm

  • Forced companies to transform or evolve their business models

  • Perished or close to being non-existence large companies which at one point were industry leaders

A more significant and higher-order business transformation program that came to light was the term "Digital Transformation." Industry leaders, academia, and many other leaders defined their own versions of how organizations should approach digital transformation. The concept started taking many forms, and technology-led organizations started positioning as their product as the lead to drive digital transformation. The positioning enabled and excited board members and executives to embark on the "Blue Pill" that adopting such technologies would be to position the company completing a digital transformation program. The order was short-lived, the companies faced the following:

  • Technologies weren't able to scale organization-wide

  • It solved a specific and not a more significant organization-specific use cases

Hence adopting technologies is just one of the cog in the wheel and not the entire picture. The recommended approach to the right digital transformation program is to define your strategy based on:

  1. Redefining the business 

  2. Enhancing customer-facing programs

  3. Improving employee engagement and increasing efficiencies

  4. Generating and evaluating new business models

  5. Setting the machine to machine engagement models

With the solve objectives to strengthen and transform the business into its entirety. In addition to core strategic framework, the essential layers which enable a digital transformation are:

  1. Data - the fundamental asset of the companies to drive, prioritize and simulate the programs

  2. People - a multi-faceted and multi-skilled team

  3. Technology - an appropriate tech stack and not a single ecosystem with a balanced subscription and open tech ecosystem

  4. Agile and DevOps - the sharp and continuous delivery mechanism

  5. AI - an enabler to exponentially impact the program

  6. Consulting - an in-house group to define, refine and project outcomes

  7. Engineering - a robust technology-enabled approach

  8. Knowledge Management - ensure retention of organizational knowledge

The layers are not independent and are like lego blocks that come together to form a structure. 

Recommended books for further reading:

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Hit Refresh

Book by Satya Nadella covering his personal life and transformation of Microsoft

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Driving Digital Strategy

One of my favorite books on Digital Transformation

2010: The decade of AI birth, 2020: The decade of AI Maturity

Last decade can be touted as the “Birth of AI”

  • 36M+ results on Google on Artificial Intelligence

  • 300+ books on Amazon

  • 11M+ scholar search results on Google Scholar search

  • 4k+ courses on Coursera.org and Udemy.com

  • Hollywood movies (Her, Iron Man and the series of Avengers, El Machina, etc.) and many more short series on subscription platforms.

  • Interest over time increased from 36 to 88 - from Google Trends

  • ~811k results on LinkedIn People Search with Artificial Intelligence term

The stats are staggering and continue to grow, overwhelm and leave each one of us wondering where would this lead to. The growth in AI (Artificial Intelligence) and its applications has given birth to many companies, conferences, academia, government, and organization led socio-political-economic initiatives, universities introducing MDPs, graduate and post-graduate programs to enable current and future students to be better equipped to manage this tsunami.

In the last 10 years, watching and being part of the industry, it feels like the industry has seen birth and growth of new kid on the block - “AI”. It almost feels like the birth of a new species which brings in opportunities and raises concerns across the various groups involved. The initial applications, actual deployments, and semi-success have instilled confidence in the possibilities this stream of technology can have on humans’ socio, political and economic growth.

The decade has also raised legitimate concerns on the applications of AI as well due to misuse or inappropriate usage. Concerns like

  1. Deep Fake

  2. Cambridge Analytica

  3. Data Privacy

  4. Technology mimicking or displacing humans

The industry has recognized such fears and incidents, and steps initiated to be put in place by authorities in power to ensure no further misuse of technology or misappropriation of facts. GDPR, PDP, CCPA are some of the data protection norms being instilled by the government and other authorities to protect consumer rights.

But the most interesting fact in the last decade has been a heightened awareness by most of the consumer groups across developed and emerging markets, which makes the AI as a center point of discussions. Today most of the AI applications have become so inconspicuous that a naked eye or an ordinary consumer that most of the apps go undetected.

The new decade is going to transform how applications of AI would be leveraged in our day to day life either personally or professionally. As technology and screens have become synonym to our day to day life, similarly, applications of AI would be seen across the human touchpoints. Here is my top 10 wishlist for AI applications for the new decade:

  1. Natural calamities and global warming

  2. Conversation and enhancement of natural resources and species

  3. Education and skilling of global citizens

  4. Personalized Medicine and Care

  5. Personalized Travel and Hospitality needs

  6. Ease of doing business or interactions with local/global government institutions

  7. DIY products and services

  8. Increased robots amongst humans

  9. Better technology and data governance

  10. Enhanced and Choice to the everyday consumer on how applications AI, technology and data usage

This is my personal perspective on how applications of AI would evolve in the new decade if you have an interesting 11 and 12 or more would like to hear about them. Chao..